1. PhD Defense 13 May 2011 Wireless Networking and Communications Group Radio Frequency Interference Modeling and Mitigation in Wireless Receivers Kapil Gulati Committee Members: Prof. Jeffrey G. Andrews Prof. Brian L. Evans (supervisor) Prof. Elmira Popova Prof. Haris Vikalo Prof. Sriram Vishwanath

2. Wireless Networking and Communications Group Outline 2  Introduction  Background  System Model  Statistical Modeling of Radio Frequency Interference  Communication Performance Analysis of Wireless Networks  Receiver Design to Mitigate Radio Frequency Interference  Conclusion

3.  Wireless transceivers Wireless Networking and Communications Group Introduction 3 Wireless Communication Sources Closely located sources Coexisting protocols Non-Communication Sources Electromagnetic radiations Computational Platform Clocks, busses, processors Co-located transceivers antenna baseband processor

4. Wireless Networking and Communications Group Introduction (cont…) 4  RFI may severely degrade communication performance  Impact of LCD noise on throughput for an IEEE 802.11g embedded wireless receiver [Shi, Bettner, Chinn, Slattery & Dong, 2006]

5. Wireless Networking and Communications Group Problem Statement 5  Designing wireless transceivers to mitigate residual RFI Guard zone Example: Dense Wi-Fi Networks Duration Channel 11 Channel 9 (a) (b) (c) (d) Residual RFI a) Co-channel b) Adjacent channel c) Out-of-platform d) In-platform

6. Wireless Networking and Communications Group Problem Statement 6  Designing wireless transceivers to mitigate residual RFI Guard zone Example: Dense Wi-Fi Networks Duration Channel 11 Channel 9 Transmit signal Pre-Filter Conventional Receiver RFI Thermal noise Distribution of Duration

7. Wireless Networking and Communications Group Approach 7 Thesis Statement: For interference-limited wireless networks, deriving closed-form non-Gaussian statistics to model tail probabilities of RFI unlocks analysis of network throughput, delay, and reliability tradeoffs and designs of physical layer receivers to increase link spectral efficiency by several bits/s/Hz, without requiring knowledge of the number, locations, or types of interference sources. Statistical Modeling of Residual RFI RFI Mitigation in MAC LayerRFI Mitigation in PHY Layer

8. Motivates: RFI Mitigation at MAC Layer Wireless Networking and Communications Group Contributions 8 Statistical Modeling of RFI Instantaneous statistics of RFI Applicability to ad hoc, cellular, local area & femtocell networks Communication Performance Analysis of Wireless Networks Decentralized wireless networks with temporal correlation Throughput, delay, and reliability RFI Mitigation at PHY Layer Pre-filtering methods mitigate RFI Contribution #1 Contribution #2 Contribution #3

9. Wireless Networking and Communications Group Statistical Models 9  Symmetric Alpha Stable (isotropic, zero-centered)  Characteristic function  Gaussian Mixture Model (isotropic, zero-centered)  Amplitude distribution  Middleton Class A (without the additive Gaussian component)

10. Wireless Networking and Communications Group Background 10 Derive RFI statistics for wider range of interference scenarios Use RFI statistics to analyze performance of networks Use a distance measure robust to impulsive statistics of RFI

11.  Interferer locations follow a spatial point process  Intended transmitter-receiver pair is  Distance apart  Sum Interference at receiver Wireless Networking and Communications Group Initial System Model 11 Pathloss Fading Narrowband Interferer emissions

12. Wireless Networking and Communications Group Contribution #1 12

13. Wireless Networking and Communications Group Instantaneous Statistics of RFI 13  Poisson Field of Interferers  Interferers  Poisson-Poisson Cluster Field of Interferers  Cluster Centers  Interferers  Closed-form statistics accurately modeling tail probability

14. Wireless Networking and Communications Group Poisson Field of Interferers 14 Cellular networks Hotspots (e.g. café) Sensor networks Ad hoc networks Dense Wi-Fi networks Networks with contention based medium access Symmetric Alpha Stable Middleton Class A (form of Gaussian Mixture)

15. Wireless Networking and Communications Group Poisson-Poisson Cluster Field of Interferers 15 Cluster of hotspots (e.g. marketplace) In-cell and out-of-cell femtocell users in femtocell networks Out-of-cell femtocell users in femtocell networks Symmetric Alpha Stable Gaussian Mixture Model

16. Wireless Networking and Communications Group Contribution #2 16

17. Wireless Networking and Communications Group System Model (Temporal Extension) 17  Network Model I (Synchronous)  User emerge at time slot k and transmit for random duration

18. Wireless Networking and Communications Group System Model (Temporal Extension) 18  Network Model II (Asynchronous)  Users can emerge at any time slot m

19. Wireless Networking and Communications Group Performance of Decentralized Networks 19  Single-hop communication performance measures  Deriving exact closed-form expressions with temporal dependence is an open problem

20. Wireless Networking and Communications Group Deriving Closed-form Performance Measures 20 Problem Formulation Performance Measures Power Amplitude and Phase Laplace TransformTail Probability Characteristic Function Required assumptions Approximate tails if closed-form not possible Key Prior WorkMy Approach Advantage: Closed-form expressions derived relatively easily Disadvantage:Asymptotically exact for low outage regimes (simulations also match in high outage regimes)

21. Wireless Networking and Communications Group Joint Temporal Statistics of Interference 21  Interference vector  Follows a 2n-dimensional symmetric alpha stable  Exact when [Ilow & Hatzinakos, 1998]  Dissertation provides theorems to show  Joint amplitude tail probabilities dominated by isotropic component (i.e., due to users active in time slots 1 through n) Depends on L Depends on fading and emissions

22. Wireless Networking and Communications Group Local Delay 22  Average time slots to have one successful transmission  Dissertation also derives  Throughput outage probability  Average network throughput Network Model II

23. Wireless Networking and Communications Group Transmission Capacity (TC) 23  Defined assuming temporal independence [Weber et al., 2005]  Extension: Network Model II Goodput: ~1.8x Improved Reliability Motivates designing MAC protocols that achieve optimum lifetime distribution

24. Wireless Networking and Communications Group Contribution #3 24

25. Wireless Networking and Communications Group Network Model I and II 25  Multivariate GMM RFI under bounded pathloss  Inphase/quadrature samples dependent but uncorrelated  Individually temporally dependent but uncorrelated  Sliding window pre-filters for single-carrier uncoded systems  Prior work on mitigating GMM noise Map to QAM Constellation Transmit Pulse Shape Filter Pre-Filter Matched Filter Demapping Bits Received Bits RFI Thermal Noise

26. Wireless Networking and Communications Group Choosing a Distance Measure for GMM 26  Correntropy Induced Metric (CIM) [Liu & Principe, 2007]  Prior work did not adapt parameter based on RFI statistics L2L2 L1L1 L0L0 L2L2 L1L1 L0L0

27. Wireless Networking and Communications Group Zero-Order Statistics of RFI to Scale CIM 27  Zero-order statistics (ZOS) [Gonzalez et al., 2006]  Use as approximate Gaussian power  Approximate lower bound on error Window of received samples Scale CIM Space

28. Wireless Networking and Communications Group Simulation Results 28 >20 dB gain 5dB

29. Wireless Networking and Communications Group Conclusions 29 Statistical Modeling of RFI Instantaneous statistics of RFI Applicability to ad hoc, cellular, local area & femtocell networks Communication Performance Analysis of Wireless Networks Decentralized wireless networks with temporal correlation Unveiled 2x “potential” improvement in network throughput RFI Mitigation at PHY Layer Pre-filtering methods mitigate RFI Improve link efficiency up to 20 dB Contribution #1 Contribution #2 Contribution #3

30. Wireless Networking and Communications Group Software Release 30 K. Gulati, M. Nassar, A. Chopra, B. Okafor, M. R. DeYoung, N. Aghasadeghi, A. Sujeeth, and B. L. Evans, "Radio Frequency Interference Modeling and Mitigation Toolbox in MATLAB", copyright © 2006-2011 by The University of Texas at Austin. Latest Toolbox Release: Version 1.6, April 2011 Website: http://users.ece.utexas.edu/~bevans/projects/rfi/software Snapshot of a demo

31. Wireless Networking and Communications Group Future Work 31  Statistical Modeling  Non-Poisson based interferer locations  Communication Performance Analysis of Wireless Networks  Multi-hop communications  Receiver Design to Mitigate RFI  MAC: Decentralized protocol to control temporal dependence  PHY: Use of ZOS scaled CIM as distance measure  Extensions to  Single-carrier MIMO  Single-antenna OFDM  MIMO-OFDM

32. Wireless Networking and Communications Group Related Publications 32 Journal Publications K. Gulati, B. L. Evans, and S. Srikanteshwara, “Interference Modeling and Mitigation in Decentralized Wireless Networks with Temporal Correlation”, in preparation. K. Gulati, R. K. Ganti, J. G. Andrews, B. L. Evans, and S. Srikanteshwara, “Throughput, Delay, and Reliability of Decentralized Wireless Networks with Temporal Correlation”, IEEE Transactions on Wireless Communications, to be submitted. K. Gulati, B. L. Evans, J. G. Andrews, and K. R. Tinsley, “Statistics of Co-Channel Interference in a Field of Poisson and Poisson-Poisson Clustered Interferers”, IEEE Transactions on Signal Processing, Vol. 58, No. 19, Dec 2010. M. Nassar, K. Gulati, M. R. DeYoung, B. L. Evans and K. R. Tinsley, “Mitigating Near- Field Interference in Laptop Embedded Wireless Transceivers”, Journal of Signal Processing Systems, Mar. 2009, invited paper. Conference Publications M. Nassar, K. Gulati, Y. Mortazavi, and B. L. Evans, “Statistical Modeling of Asynchronous Impulsive Noise in Powerline Communication Networks”, Proc. IEEE Global Communications Conf., Dec. 5-9, 2011, Houston, Texas, USA, submitted.

33. Wireless Networking and Communications Group Related Publications 33 Conference Publications (cont…) K. Gulati, B. L. Evans, and K. R. Tinsley, “Statistical Modeling of Co-Channel Interference in a Field of Poisson Distributed Interferers”, Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., Mar. 14-19, 2010, Dallas, Texas USA. K. Gulati, A. Chopra, B. L. Evans, and K. R. Tinsley, “Statistical Modeling of Co-Channel Interference”, Proc. IEEE Int. Global Communications Conf., Nov. 30-Dec. 4, 2009, Honolulu, Hawaii. A. Chopra, K. Gulati, B. L. Evans, K. R. Tinsley, and C. Sreerama, “Performance Bounds of MIMO Receivers in the Presence of Radio Frequency Interference”, Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., Apr. 19-24, 2009, Taipei, Taiwan. K. Gulati, A. Chopra, R. W. Heath, Jr., B. L. Evans, K. R. Tinsley, and X. E. Lin, “MIMO Receiver Design in the Presence of Radio Frequency Interference”, Proc. IEEE Int. Global Communications Conf., Nov. 30-Dec. 4th, 2008, New Orleans, LA USA. M. Nassar, K. Gulati, A. K. Sujeeth, N. Aghasadeghi, B. L. Evans and K. R. Tinsley, “Mitigating Near-Field Interference in Laptop Embedded Wireless Transceivers”, Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., Mar. 30-Apr. 4, 2008, Las Vegas, NV USA.

34. Wireless Networking and Communications Group 34 Thanks !

35. Wireless Networking and Communications Group Selected References 35 RFI Modeling 1.D. Middleton, “Non-Gaussian noise models in signal processing for telecommunications: New methods and results for Class A and Class B noise models”, IEEE Trans. Info. Theory, vol. 45, no. 4, pp. 1129-1149, May 1999. 2.K. Furutsu and T. Ishida, “On the theory of amplitude distributions of impulsive random noise,” J. Applied Physics, vol. 32, no. 7, pp. 1206–1221, 1961. 3.J. Ilow and D. Hatzinakos, “Analytic alpha-stable noise modeling in a Poisson field of interferers or scatterers”, IEEE Trans. on Signal Proc., vol. 46, no. 6, pp. 1601-1611, Jun. 1998. 4.E. S. Sousa, “Performance of a spread spectrum packet radio network link in a Poisson field of interferers,” IEEE Trans. on Info. Theory, vol. 38, no. 6, pp. 1743–1754, Nov. 1992. 5.X. Yang and A. Petropulu, “Co-channel interference modeling and analysis in a Poisson field of interferers in wireless communications,” IEEE Trans. on Signal Proc., vol. 51, no. 1, pp. 64–76, Jan. 2003. 6.E. Salbaroli and A. Zanella, “Interference analysis in a Poisson field of nodes of finite area,” IEEE Trans. on Vehicular Tech., vol. 58, no. 4, pp. 1776–1783, May 2009. 7.M. Z. Win, P. C. Pinto, and L. A. Shepp, “A mathematical theory of network interference and its applications,” Proc. of the IEEE, vol. 97, no. 2, pp. 205–230, Feb. 2009.

36. Wireless Networking and Communications Group Selected References 36 Parameter Estimation 1.S. M. Zabin and H. V. Poor, “Efficient estimation of Class A noise parameters via the EM [Expectation-Maximization] algorithms”, IEEE Trans. Info. Theory, vol. 37, no. 1, pp. 60-72, Jan. 1991. 2.G. A. Tsihrintzis and C. L. Nikias, "Fast estimation of the parameters of alpha-stable impulsive interference", IEEE Trans. Signal Proc., vol. 44, Issue 6, pp. 1492-1503, Jun. 1996. Communication Performance of Wireless Networks 1.M. Haenggi and R. K. Ganti, “Interference in large wireless networks,” in Foundations and Trends in Networking. Now Publishers Inc., Dec. 2008, vol. 3, no. 2, pp. 127-248. 2.F. Baccelli and B. Blaszczyszyn, “Stochastic geometry and wireless networks, volume 1 – theory”, in Foundations and Trends in Networking. Now Publishers Inc., Mar. 2009, vol. 3, no. 3-4, pp. 249- 449. 3.F. Baccelli and B. Blaszczyszyn, “Stochastic geometry and wireless networks, volume 2 – applications”, in Foundations and Trends in Networking. Now Publishers Inc., Mar. 2009, vol. 4, no. 1-2, pp. 1-312. 4.R. Ganti and M. Haenggi, “Interference and outage in clustered wireless ad hoc networks,” IEEE Trans. on Info. Theory, vol. 55, no. 9, pp. 4067–4086, Sep. 2009. 5.A. Hasan and J. G. Andrews, “The guard zone in wireless ad hoc networks,” IEEE Trans. on Wireless Comm., vol. 4, no. 3, pp. 897–906, Mar. 2007.

37. Wireless Networking and Communications Group Selected References 37 Communication Performance of Wireless Networks (cont…) 6.X. Yang and G. de Veciana, “Inducing multiscale spatial clustering using multistage MAC contention in spread spectrum ad hoc networks,” IEEE/ACM Trans. on Networking, vol. 15, no. 6, pp. 1387–1400, Dec. 2007. 7.S. Weber, X. Yang, J. G. Andrews, and G. de Veciana, “Transmission capacity of wireless ad hoc networks with outage constraints,” IEEE Trans. on Info. Theory, vol. 51, no. 12, pp. 4091-4102, Dec. 2005. 8.S. Weber, J. G. Andrews, and N. Jindal, “The effect of fading, channel inversion, and threshold scheduling on ad hoc networks,” IEEE Trans. on Info. Theory, vol. 53, no. 11, pp. 4127-4149, Nov. 2007. 9.J. G. Andrews, S. Weber, M. Kountouris, and M. Haenggi, “Random access transport capacity,” IEEE Trans. On Wireless Comm., vol. 9, no. 6, pp. 2101-2111, Jun. 2010. 10.M. Haenggi, “Local delay in static and highly mobile Poisson networks with ALOHA," in Proc. IEEE Int. Conf. on Comm., Cape Town, South Africa, May 2010. 11.F. Baccelli and B. Blaszczyszyn, “A New Phase Transitions for Local Delays in MANETs,” in Proc. of IEEE Int. Conf. on Computer Comm., San Diego, CA, Mar. 14-19 2010, pp. 1-6. 12.R. K. Ganti and M. Haenggi, “Spatial and Temporal correlation of the interference in ALOHA ad hoc networks,” IEEE Comm. Letters, vol. 13, no. 9, pp. 631-633, Sep. 2009. 13.H. Inaltekin, S. B. Wicker, M. Chiang, and H. V. Poor, "On unbounded path-loss models: effects of singularity on wireless network performance," IEEE Journal on Selected Areas in Comm., vol. 27, no. 7, pp. 1078-1092, Sep. 2009.

38. Wireless Networking and Communications Group Selected References 38 Receiver Design to Mitigate RFI 1.A. Spaulding and D. Middleton, “Optimum Reception in an Impulsive Interference Environment- Part I: Coherent Detection”, IEEE Trans. Comm., vol. 25, no. 9, Sep. 1977 2.J.G. Gonzalez and G.R. Arce, “Optimality of the Myriad Filter in Practical Impulsive-Noise Environments”, IEEE Trans. on Signal Proc., vol. 49, no. 2, Feb 2001 3.S. Ambike, J. Ilow, and D. Hatzinakos, “Detection for binary transmission in a mixture of Gaussian noise and impulsive noise modelled as an alpha-stable process,” IEEE Signal Proc. Letters, vol. 1, pp. 55–57, Mar. 1994. 4.G. R. Arce, Nonlinear Signal Processing: A Statistical Approach, John Wiley & Sons, 2005. 5.Y. Eldar and A. Yeredor, “Finite-memory denoising in impulsive noise using Gaussian mixture models,” IEEE Trans. on Circuits and Systems II: Analog and Digital Signal Proc., vol. 48, no. 11, pp. 1069-1077, Nov. 2001. 6.J. H. Kotecha and P. M. Djuric, “Gaussian sum particle filtering,” IEEE Trans. on Signal Proc., vol. 51, no. 10, pp. 2602-2612, Oct. 2003. 7.J. G. Gonzalez, J. L. Paredes, and G. R. Arce, "Zero-order statistics: A mathematical framework for the processing and characterization of very impulsive signals," IEEE Trans. on Signal Proc., vol. 54, no. 10, pp. 3839-3851, Oct. 2006.

39. Wireless Networking and Communications Group Selected References 39 Receiver Design to Mitigate RFI 8.J. G. Gonzalez, J. L. Paredes, and G. R. Arce, "Zero-order statistics: A mathematical framework for the processing and characterization of very impulsive signals," IEEE Trans. on Signal Proc., vol. 54, no. 10, pp. 3839-3851, Oct. 2006. 9.W. Liu, P. P. Pokharel, and J. C. Principe, "Correntropy: Properties and applications in non-Gaussian signal processing," IEEE Trans. on Signal Proc., vol. 55, no. 11, pp. 5286-5298, 2007. 10.W. Liu, P. P. Pokharel, and J. C. Principe, "Error entropy, correntropy and M-estimation," in Proc. IEEE Workshop on Machine Learning for Signal Proc., Arlington, VA, Sep. 6-8 2006, pp. 179-184. 11.J. Haring and A. J. H. Vinck, "Iterative decoding of codes over complex numbers for impulsive noise channels," IEEE Trans. on Info. Theory, vol. 49, no. 5, pp. 1251-1260, May 2003.

40. Wireless Networking and Communications Group Backup Slides 40  Introduction  Summary of interference mitigation methods  Interference avoidance, alignment, and cancellation methods  Femtocell networks  Statistical Modeling of RFI  Impact of RFI  Computational platform noise modeling results  Transients in digital FIR filters  Spatial Poisson Point Process  Poisson field of interferers  Poisson-Poisson cluster field of interferers Backup

41. Wireless Networking and Communications Group Backup Slides (cont…) 41  Communication Performance of Wireless Networks  Performance Analysis of Wireless Networks  Ad hoc networks with guard zones  Local Delay  Decentralized networks with temporal correlation Local Delay Throughput Outage Probability Transmission Capacity  Parameter Estimation  Expectation maximization overview  Extreme order statistics based estimator for Alpha Stable Backup

42. Wireless Networking and Communications Group Backup Slides (cont…) 42  Receiver Design to Mitigate RFI  Gaussian mixture vs. Alpha Stable  Mitigating RFI in SISO systems  Mitigating RFI in 2x2 MIMO systems  Pre-filtering methods to mitigate RFI  Pre-filtering methods to mitigate GMM distributed RFI  Joint temporal statistics  Distance Measure  Correntropy Induced Metric  Zero-order Statistics Backup

43. Wireless Networking and Communications Group Backup Slides (cont…) 43  Pre-filtering methods to mitigate GMM RFI (cont…)  Pre-filters  Computational complexity  Applications of ZOS scaled CIM space OFDM Turbo Decoders Backup

44. Wireless Networking and Communications Group Interference Mitigation Techniques 44 Return

45. Wireless Networking and Communications Group Interference Mitigation Techniques (cont…) 45  Interference avoidance  CSMA / CA  Interference alignment  Example: [Cadambe & Jafar, 2007] Return

46. Wireless Networking and Communications Group Interference Mitigation Techniques (cont…) 46  Interference cancellation Ref: J. G. Andrews, ”Interference Cancellation for Cellular Systems: A Contemporary Overview”, IEEE Wireless Communications Magazine, Vol. 12, No. 2, pp. 19-29, April 2005 Return

47. Wireless Networking and Communications Group Femtocell Networks 47 Reference: V. Chandrasekhar, J. G. Andrews and A. Gatherer, "Femtocell Networks: a Survey", IEEE Communications Magazine, Vol. 46, No. 9, pp. 59-67, September 2008 Return

48. Wireless Networking and Communications Group Common Spectral Occupancy 48 Return

49. Wireless Networking and Communications Group Impact of RFI 49  Calculated in terms of desensitization (“desense”)  Interference raises noise floor  Receiver sensitivity will degrade to maintain SNR  Desensitization levels can exceed 10 dB for 802.11a/b/g due to computational platform noise [J. Shi et al., 2006] Case Sudy: 802.11b, Channel 2, desense of 11dB More than 50% loss in range Throughput loss up to ~3.5 Mbps for very low receive signal strengths (~ -80 dbm) 49 Return

50. Wireless Networking and Communications Group Impact of LCD clock on 802.11g 50  Pixel clock 65 MHz  LCD Interferers and 802.11g center frequencies 50 Return

51. Results on Measured RFI Data 51  25 radiated computer platform RFI data sets from Intel  50,000 samples taken at 100 MSPS Wireless Networking and Communications Group Return

52. Wireless Networking and Communications Group Results on Measured RFI Data 52  For measurement set #23 Return

53. Wireless Networking and Communications Group Transients in Digital FIR Filters 53 25-Tap FIR Filter Low pass Stopband freq. 0.22 (normalized) InputOutput Freq = 0.16 Interference duration = 10 * 1/0.22 Interference duration = 100 x 1/0.22 Transients Transients Significant w.r.t. Steady StateTransients Ignorable w.r.t. Steady State Return

54. Wireless Networking and Communications Group Homogeneous Spatial Poisson Point Process 54 Return

55. Wireless Networking and Communications Group Poisson Field of Interferers 55  Applied to wireless ad hoc networks, cellular networks Return

56. Wireless Networking and Communications Group Poisson Field of Interferers 56  Interferers distributed over parametric annular space  Log-characteristic function Return

57. Wireless Networking and Communications Group Poisson Field of Interferers 57 Return

58. Wireless Networking and Communications Group Poisson Field of Interferers 58  Simulation Results (tail probability) Gaussian and Middleton Class A models are not applicable since mean intensity is infinite Case I: Entire Plane Case III: Infinite-area with guard zone Return

59. Wireless Networking and Communications Group Poisson Field of Interferers 59  Simulation Results (tail probability) Case II: Finite area annular region Return

60. Wireless Networking and Communications Group Poisson-Poisson Cluster Field of Interferers 60  Applied to femtocell networks, cellular and ad hoc networks with user clustering  Clustering due to  Geographical factors (femtocell networks)  Medium Access Control (MAC) layer protocols [Yang & de Veciana, 2007]  Prior Work  Closed form amplitude distribution not derived Return

61. Wireless Networking and Communications Group Poisson-Poisson Cluster Field of Interferers 61  Cluster centers distributed as spatial Poisson process over  Interferers distributed as spatial Poisson process Return

62. Wireless Networking and Communications Group Poisson-Poisson Cluster Field of Interferers 62  Log-Characteristic function Return

63. Wireless Networking and Communications Group Poisson-Poisson Cluster Field of Interferers 63  Simulation Results (tail probability) Gaussian and Gaussian mixture models are not applicable since mean intensity is infinite Case I: Entire Plane Case III: Infinite-area with guard zone Return

64. Wireless Networking and Communications Group Poisson-Poisson Cluster Field of Interferers 64  Simulation Results (tail probability) Case II: Finite area annular region Return

65. Summary of Contribution #1 Sensor networks Ad hoc networks Dense Wi-Fi networks Cluster of hotspots (e.g. marketplace) In-cell and out-of-cell femtocell users Out-of-cell femtocell users Cellular networks Hotspots (e.g. café) Symmetric Alpha Stable Poisson field of interferers Ad hoc networks Cellular networks Poisson-Poisson Cluster field of interferers Femtocell networks Gaussian Mixture Model 65 Wireless Networking and Communications Group Return

66. Wireless Networking and Communications Group Performance Analysis of Wireless Networks 66  Interference statistics useful for  Communication performance analysis of wireless networks  Deriving network strategies to improve performance Both Physical (PHY) and Medium Access Control (MAC) Layer  Communication performance measures Return

67. Wireless Networking and Communications Group Performance Analysis of Wireless Networks (cont…) 67  Proposed Contribution #2 [future work] Return

68. Wireless Networking and Communications Group Ad hoc Networks with Guard Zones (GZs) 68  System Model Return

69. Wireless Networking and Communications Group Point Processes for Networks with GZs 69  Modified Matern hardcore [Baccelli, 2009]  Neighbor set (received power based) [Baccelli, 2009]  Neighbor set (distance based) [Hasan & Andrews, 2007]  Limitation: Underestimates intensity  Simple Sequential Inhibition [Busson, Chelius & Gorce, 2009]  Even intensity expression not known 1 2 3 Return

70. Wireless Networking and Communications Group Ad hoc networks with GZ: Prior Work 70  Transmission Capacity, Optimum GZ size [Hasan & Andrews, 2007]  AS1: Poisson distributed  AS2: Sum interference is Gaussian  AS3: Distance based GZ creation  Limitation: Gaussian assumption may not be valid  Plan of Work: Use Middleton Class A statistics Return

71. Wireless Networking and Communications Group Ad hoc networks with GZ: Prior Work 71  Outage Probability [Baccelli, 2009]  AS1: Poisson distributed  AS2: Received power based GZ creation  Limitation: Closed form for Rayleigh fading only Return

72. Wireless Networking and Communications Group Probability of Successful Transmission 72 Return

73. Wireless Networking and Communications Group Local Delay: Definition 73  Expected time slots till packet is successfully received  Probability of success  Conditional Local Delay – Geometric with mean  Local Delay Return

74. Wireless Networking and Communications Group Local Delay: Prior Work 74  Prior Work [Haenggi, 2010][Baccelli, 2010]  Phase transition for static Poisson networks  Due to SINR model for connectivity  Avoided by using adaptive coding [Baccelli, 2010] Return

75. Wireless Networking and Communications Group Local Delay 75 Return

76. Wireless Networking and Communications Group Local Delay (cont…) 76 Network Model II Network Model I Return

77. Wireless Networking and Communications Group Throughput Outage Probability 77  Derived closed-form expressions using joint tail probability Network Model II Return

78. Wireless Networking and Communications Group Throughput Outage Probability (cont…) 78 Network Model I Return

79. Wireless Networking and Communications Group Average Network Throughput 79 Return Network Model II

80. Wireless Networking and Communications Group Transmission Capacity 80  Defined assuming temporal independence [Weber et al., 2005]  Extension: Network Model II Goodput: ~1.8x Improved Reliability Motivates designing MAC protocols that achieve optimum lifetime distribution Return

81. Wireless Networking and Communications Group Transmission Capacity (cont…) 81  Optimal Lifetime distribution (via numerical optimization) Network Model II Using fmincon function in MATLAB Active set algorithm Return

82. Wireless Networking and Communications Group Expectation Maximization Overview 82 Return

83. Wireless Networking and Communications Group Extreme Order Statistics 83 Return

84. Wireless Networking and Communications Group Parameter Estimators for Alpha Stable 84 0 < p < α Return

85. Wireless Networking and Communications Group Particle Filtering 85 Ref: P. Djuric et. al., “Particle Filtering,” IEEE Signal Processing Magazine, vol. 20, no. 5, September 2003, pp: 19-38. Return

86. Wireless Networking and Communications Group Gaussian Mixture vs. Alpha Stable 86  Gaussian Mixture vs. Symmetric Alpha Stable Gaussian MixtureSymmetric Alpha Stable ModelingInterferers distributed with Guard zone around receiver (actual or virtual due to PL) Interferers distributed over entire plane Pathloss Function With GZ: singular / non-singular Entire plane: non-singular Singular form Thermal Noise Easily extended (sum is Gaussian mixture) Not easily extended (sum is Middleton Class B) OutliersEasily extended to include outliersDifficult to include outliers Return

87. Wireless Networking and Communications Group RFI Mitigation in SISO Systems 87 Mitigation of computational platform noise in single carrier, single antenna systems [Nassar, Gulati, DeYoung, Evans & Tinsley, ICASSP 2008, JSPS 2009] Return

88. Wireless Networking and Communications Group Filtering and Detection 88 Pulse Shaping Pre-Filtering Matched Filter Detection Rule Impulsive Noise Middleton Class A noise Symmetric Alpha Stable noise Filtering  Wiener Filtering (Linear) Detection  Correlation Receiver (Linear)  Bayesian Detector [Spaulding & Middleton, 1977]  Small Signal Approximation to Bayesian detector [Spaulding & Middleton, 1977] Filtering  Myriad Filtering  Optimal Myriad [Gonzalez & Arce, 2001]  Selection Myriad  Hole Punching [Ambike et al., 1994] Detection  Correlation Receiver (Linear)  MAP approximation [Kuruoglu, 1998] Assumption Multiple samples of the received signal are available N Path Diversity [Miller, 1972] Oversampling by N [Middleton, 1977] Assumption Multiple samples of the received signal are available N Path Diversity [Miller, 1972] Oversampling by N [Middleton, 1977] Return

89. Wireless Networking and Communications Group Results: Class A Detection 89 Pulse shape Raised cosine 10 samples per symbol 10 symbols per pulse Channel A = 0.35  = 0.5 × 10 -3 Memoryless Communication Performance Binary Phase Shift Keying Return

90. Wireless Networking and Communications Group Results: Alpha Stable Detection 90 Use dispersion parameter  in place of noise variance to generalize SNR Communication Performance Same transmitter settings as previous slide Return

91. Wireless Networking and Communications Group RFI Mitigation in 2x2 MIMO Systems 91 2 x 2 MIMO receiver design in the presence of RFI [Gulati, Chopra, Heath, Evans, Tinsley & Lin, Globecom 2008] Return

92. 92Wireless Networking and Communications Group Bivariate Middleton Class A Model  Joint spatial distribution Return

93. 93Wireless Networking and Communications Group Results on Measured RFI Data  50,000 baseband noise samples represent broadband interference Marginal PDFs of measured data compared with estimated model densities Return

94. 94  2 x 2 MIMO System  Maximum Likelihood (ML) receiver  Log-likelihood function Wireless Networking and Communications Group System Model Sub-optimal ML Receivers approximate Return

95. Wireless Networking and Communications Group Sub-Optimal ML Receivers 95  Two-piece linear approximation  Four-piece linear approximation chosen to minimize Approximation of Return

96. 96Wireless Networking and Communications Group Results: Performance Degradation  Performance degradation in receivers designed assuming additive Gaussian noise in the presence of RFI Simulation Parameters 4-QAM for Spatial Multiplexing (SM) transmission mode 16-QAM for Alamouti transmission strategy Noise Parameters: A = 0.1,  1 = 0.01,  2 = 0.1,  = 0.4 Severe degradation in communication performance in high-SNR regimes Return

97. Wireless Networking and Communications Group Results: RFI Mitigation in 2 x 2 MIMO 97 Improvement in communication performance over conventional Gaussian ML receiver at symbol error rate of 10 -2 Communication Performance (A = 0.1,  1 = 0.01,  2 = 0.1,  = 0.4) Return

98. Wireless Networking and Communications Group Results: RFI Mitigation in 2 x 2 MIMO 98 Complexity Analysis Complexity Analysis for decoding M-level QAM modulated signal Communication Performance (A = 0.1,  1 = 0.01,  2 = 0.1,  = 0.4) Return

99. Wireless Networking and Communications Group Pre-filtering Methods to Mitigate RFI 99  Pre-filtering based on statistical models  Gaussian Mixture Filtering (MMSE objective function)  Non-linear combination of banks of Weiner filter  Non-linear combination of banks of Gaussian Particle Filters Return

100. Wireless Networking and Communications Group Pre-filtering for Gaussian mixture noise 100  Closed form objective function or filter structure for BER optimality not known  Finite-memory minimum mean squared error (MMSE) filter [Eldar & Yeredor, 2001]  Filtering Gaussian signal in Gaussian mixture noise  Non-linear combination of bank of Wiener filters  Good for highly impulsive noise  Gaussian sum particle filters [Kotecha & Djuric, 2003]  Bank of Gaussian particle filters  Order-statistic filtering  Linear combination of ordered data Return

101. Wireless Networking and Communications Group Order Statistic Filtering 101  Linear combination of order statistics Return

102. Wireless Networking and Communications Group Joint Temporal Statistics 102  Bounded Pathloss Function Network Model II Return

103. Wireless Networking and Communications Group Distance Measure 103  Example: Constant signal in noise  Optimal distance measure depends on noise statistics  Not known for GMM noise Impulsive NoiseNearly Gaussian Noise Return

104. Wireless Networking and Communications Group Correntropy Induced Metric (CIM) 104  Sample estimator of Correntropy [Liu and Principe, 2007] Return

105. Wireless Networking and Communications Group Zero-Order Statistics 105 Return

106. Wireless Networking and Communications Group Zero-Order Statistics (cont…) 106  “Gaussian part” of non-Gaussian random process Return

107. Wireless Networking and Communications Group Pre-filters 107 Sliding window Selection Pre-filter Modified L l Pre-filter Selection Pre-filter Adaptive Update with J(error) Training data L l Pre-filter Return

108. Wireless Networking and Communications Group Simulation Results 108 Return

109. Wireless Networking and Communications Group Simulation Results (cont…) 109 Return

110. Wireless Networking and Communications Group Simulation Results (cont…) 110  Gaussian distributed interference Return

111. Wireless Networking and Communications Group Computational Complexity 111 Return

112. Wireless Networking and Communications Group Computational Complexity (cont…) 112  Zero-order statistics from N received samples  N-1 multiplications  1 table lookup to evaluate N th root  Correntropy Induced Metric (additional over L2 norm)  1 multiplication  1 exponential evaluation (table lookup)  1 subtraction  1 square root evaluation (table lookup) Not required if max/min operation on distance is being performed Return

113. Wireless Networking and Communications Group Pre-filtering in OFDM Systems 113  OFDM transmissions with nyquist sampling at receiver Return

114. Wireless Networking and Communications Group Pre-filtering in OFDM Systems (cont…) 114  OFDM transmissions with 7x oversampling at receiver Return

115. Turbo Decoder Decoder 1 Parity 1 Systematic Data Decoder 2 Parity 2 - - - - A-priori Information Depends on channel statistics Independent of channel statistics Extrinsic Information 115 Wireless Networking and Communications Group Return

116. Wireless Networking and Communications Group Turbo Decoder (cont…) 116  Gaussian noise  Non-Gaussian noise (requires knowledge of noise statistics)  Proposed: Based on ZOS scaled CIM space S-CIM instead of L 2 norm Return

117. Wireless Networking and Communications Group Turbo Decoder (Preliminary Results) 117 Return

118. Wireless Networking and Communications Group ESPL Research in RFI Modeling and Mitigation 118 ESPL Research in RFI Modeling and Mitigation RFI Modeling StudentMethodsAntennasCarrierMultipathTime SamplesMeasured Fitting KapilStatistical PhysicalSingle NoDependentComputational Platform Noise AdityaStatistical PhysicalMultipleSingleYesIndependent MarcelStatistical PhysicalSingleMultipleNoDependentComputational Platform Noise Receiver Design in the Presence of RFI StudentAntennasCarrierCodingMultipathFocus KapilSingle / MultipleSingleNo Filtering methods AdityaSingle / MultipleSingleNoYesDetection methods MarcelSingle / MultipleSingle / MulitpleYesNoFiltering and decoding Multipath indicates if multiple paths from interferer to receiver. Measured Fitting indicates the pure simulation-based measured fitting results, but does not include possible results from measured data from the underlying model assumed: (a) co-channel / adjacent channel (Kapil) (b) multi-antenna (Aditya) (c) correlated fitting (Marcel)). Return